Technology and the environment: an evolutionary approach to sustainable technological change

(WP 02/04 Clave pdf) The results of our model show that it would be advisable to undertake policies expressly aimed at the process of sustainable technological change in a way that is complementary to the conventional equilibrium oriented environmental policies. In short, the main objectives of this paper are to understand more fully the dynamics of the process of technological change, its role in sustainable development, and to assess the implications of this dynamic approach to techno-environmental policy. To achieve these goals we have developed an agent based model, using distributed artificial intelligence concepts drawn from the general methodology of social simulation.Agent-based models, Evolutionary models, Lock-in , Standardization, Technology difussion, Sustainability

IE WP 02/04 Technology and the environment: an evolutionary approach to sustainable technological change

Can the individual actions of agents spontaneously move the system out of a state where it is locked into an environmentally inferior technology, or is coordination from outside the system necessary in the form of public intervention? More importantly, even if the system were able to make the transition unaided, could market coordination mechanisms play an important role? The results of our model show that it would be advisable to undertake policies expressly aimed at the process of sustainable technological change –applying an ex ante (precautionary) approach– in a way that is complementary to the conventional equilibrium oriented environmental policies. The nature of these policies and how they might be implemented are questions we will address from this novel approach to the concept of sustainable development. In short, the main objectives of this paper are to understand more fully the dynamics of the process of technological change, its role in sustainable development, and to assess the implications of this dynamic approach to techno-environmental policy. To achieve these goals we have developed an agent based model (ABM), using distributed artificial intelligence (DAI) concepts drawn from the general methodology of social simulation.Technology diffusion; standardization; lock-in; sustainability; precautionary approach; evolutionary models; agent-based models

Maintenance policy for two-stage deteriorating mode system based on cumulative damage model

For the system degradation process undergoing a sudden change, optimal maintenance policies were developed using the cumulative damage model and two-stage degradation modeling. Single shock damage value and the number of shock times are assumed to be normal distribution and homogeneous Poisson process, respectively. On this basis, average long-run cost rate of a renewal cycle was modeled with considering the probabilities of corrective, preventive and continuous monitoring, respectively. In order to develop an optimal policy, four types of maintenance policies (i.e., global, time-depended, adaptive and simplified adaptive policies) were analyzed with different alarm thresholds and inter-inspection time. Influence analysis of different parameters for maintenance policy was given, where different maintenance policies were compared in terms of average long-run cost rate. In addition, the impacts of degradation model parameters (i.e., change-point distribution, shock strength, shock frequency) on the average long-run cost rate were analyzed. Finally, maintenance policy for gearbox degradation experiment was analyzed in case study

Reliability and Condition-Based Maintenance Analysis of Deteriorating Systems Subject to Generalized Mixed Shock Model

For successful commercialization of evolving devices (e.g., micro-electro-mechanical systems, and biomedical devices), there must be new research focusing on reliability models and analysis tools that can assist manufacturing and maintenance of these devices. These advanced systems may experience multiple failure processes that compete against each other. Two major failure processes are identified to be deteriorating or degradation processes (e.g., wear, fatigue, erosion, corrosion) and random shocks. When these failure processes are dependent, it is a challenging problem to predict reliability of complex systems. This research aims to develop reliability models by exploring new aspects of dependency between competing risks of degradation-based and shock-based failure considering a generalized mixed shock model, and to develop new and effective condition-based maintenance policies based on the developed reliability models. In this research, different aspects of dependency are explored to accurately estimate the reliability of complex systems. When the degradation rate is accelerated as a result of withstanding a particular shock pattern, we develop reliability models with a changing degradation rate for four different shock patterns. When the hard failure threshold reduces due to changes in degradation, we investigate reliability models considering the dependence of the hard failure threshold on the degradation level for two different scenarios. More generally, when the degradation rate and the hard failure threshold can simultaneously transition multiple times, we propose a rich reliability model for a new generalized mixed shock model that is a combination of extreme shock model, δ-shock model and run shock model. This general assumption reflects complex behaviors associated with modern systems and structures that experience multiple sources of external shocks. Based on the developed reliability models, we introduce new condition-based maintenance strategies by including various maintenance actions (e.g., corrective replacement, preventive replacement, and imperfect repair) to minimize the expected long-run average maintenance cost rate. The decisions for maintenance actions are made based on the health condition of systems that can be observed through periodic inspection. The reliability and maintenance models developed in this research can provide timely and effective tools for decision-makers in manufacturing to economically optimize operational decisions for improving reliability, quality and productivity.Industrial Engineering, Department o